• No se han encontrado resultados

Technological contributions to imaging radars in the millimeter-wave band

N/A
N/A
Protected

Academic year: 2020

Share "Technological contributions to imaging radars in the millimeter-wave band"

Copied!
390
0
0

Texto completo

(1)UNIVERSIDAD POLITÉCNICA DE MADRID ESCUELA TÉCNICA SUPERIOR DE INGENIEROS DE TELECOMUNICACIÓN. ETSIT. ESCUELA TECNICA SUPERIOR DE INGENIEROS DE TELECOMUNICACIÓN. TECHNOLOGICAL CONTRIBUTIONS TO IMAGING RADARS IN THE MILLIMETER-WAVE BAND. TESIS DOCTORAL. Federico Antonio Garcı́a Rial INGENIERO DE TELECOMUNICACIÓN. 2019.

(2)

(3)

(4)

(5) DEPARTAMENTO DE SISTEMAS, SEÑALES Y RADIOCOMUNICACIONES. Escuela Técnica Superior de Ingenieros de Telecomunicación Universidad Politécnica de Madrid. TECHNOLOGICAL CONTRIBUTIONS TO IMAGING RADARS IN THE MILLIMETER-WAVE BAND. TESIS DOCTORAL. Autor:. Federico Antonio Garcı́a Rial Ingeniero de Telecomunicación Universidad Politécnica de Madrid. Director:. Jesús Grajal de la Fuente Catedrático del Dpto. de Señales, Sistemas y Radiocomunicaciones Universidad Politécnica de Madrid. 2019.

(6)

(7) TESIS DOCTORAL. TECHNOLOGICAL CONTRIBUTIONS TO IMAGING RADARS IN THE MILLIMETER-WAVE BAND. AUTOR: Federico Antonio Garcı́a Rial DIRECTOR: Jesús Grajal de la Fuente. Tribunal nombrado por el Mgfco. y Excmo. Sr. Rector de la Universidad Politécnica de Madrid, el dı́a ...... de ...................................... de 2019.. PRESIDENTE:.......................................................................................... SECRETARIO:........................................................................................... VOCAL:.................................................................................................. VOCAL:.................................................................................................. VOCAL:.................................................................................................. SUPLENTE:............................................................................................. SUPLENTE:............................................................................................. Realizado el acto de defensa y lectura de la Tesis el dı́a ...... de ...................................... de 2019, en Madrid.. Calificación: EL PRESIDENTE. LOS VOCALES. EL SECRETARIO.

(8)

(9) Abstract Recent research in the millimeter- and submillimeter-wave frequency bands has driven the maturity of its technologies to a level where integrated and commercial radiofrequency components are now readily available. Due to this development the fundamental applications of these frequency bands (like spectroscopy, radioastronomy, and wireless commnunication) have benefited from increasing performance, at a fraction of the cost. This is also the case for imaging applications, such as concealed weapon detection, which is progressively becoming a viable alternative to traditional security screenings. Nonetheless, some technological progress is still needed for security imaging in this frequency range to fully reach its potential, specially regarding critical and diverse areas such as component cost, power consumption, imaging speed, or privacy concerns. The main goal of this Ph.D. thesis can be found within this framework, and it is to develop those technological improvements and solutions necessary to enhance the performance of security imaging radars in the millimeter- and submillimeter-wave bands. This objective has been accomplished in two particular radar systems: an active imager at 300 GHz (developed previously to the start of this thesis), and a hybrid system using active and passive sensors at 122 GHz and 94 GHz, respectively. Throughout the development, a special emphasis is placed on achieving the desired results with the use of commercially-available components, in an effort to truly provide a viable alternative. Although both systems are meant for security screening purposes, their radiofrequency and processing architectures present various differences according to their operational requirements. In addition to their security-related function, both systems have also been employed in other applications, such as vital sign monitoring. The thesis has been entirely carried out at the Grupo de Microondas y Radar of the Universidad Politécnica de Madrid, and is divided into two main parts. The first half deals with the improvements made to the 300 GHz imaging system, including a real-time signal and image processing architecture and the initial tests of a multistatic configuration. The second half is focused on the development of the hybrid imager at 122 GHz and 94 GHz, detailing individual sensor characterizations, simultaneous performance, and an image fusion technique to present the captured data. In each section, pending improvement actions are also studied and described for future research and work.. Keywords: Concealed weapon detection, imaging radar, millimeter-wave, submillimeter-wave, terahertz..

(10)

(11) Resumen Investigaciones recientes en la banda de frecuencias de ondas milimétricas y submilimétricas han impulsado la madurez de sus tecnologı́as hasta un nivel donde componentes de radiofrecuencia comerciales e integrados ya están disponibles. Debido a este desarrollo las aplicaciones fundamentales de estas bandas de frecuencia (como la espectroscopia, la radioastronomı́a, y las comunicaciones inalámbricas) se han beneficiado de un rendimiento en incremento, por una fracción del coste habitual. Este es también el caso de las aplicaciones de imagen, como la detección de amenazas ocultas, que está progresivamente convirtiéndose en una alternativa viable a las inspecciones de seguridad tradicionales. Sin embargo, aún es necesario cierto progreso tecnológico para que las aplicaciones de seguridad basadas en imágenes en este rango de frecuencias alcancen su potencial completo, especialmente en áreas tan crı́ticas y diversas como el coste de componentes, el consumo, la velocidad de refresco de imágenes, o las preocupaciones por privacidad. La meta principal de esta tésis doctoral se puede encontrar dentro de este marco, y es desarrollar las mejoras y soluciones tecnológicas necesarias para elevar el rendimiento de los radares de imagen para seguridad en las bandas de milimétricas y submilimétricas. Este objetivo se ha cumplido para dos sistemas radar en particular: un sistema activo a 300 GHz (desarrollado previamente a esta tesis), y un sistema hı́brido usando sensores activos y pasivos a 122 GHz y 94 GHz, respectivamente. Durante el desarrollo, se ha puesto un énfasis especial en alcanzar los resultados deseados mediante el uso de componentes comercialmente disponibles, en un esfuerzo por verdaderamente proporcionar una alternativa viable. Aunque ambos sistemas están pensados para las inspecciones de seguridad, sus arquitecturas de radiofrecuencia y procesado presentan varias diferencias según sus requisitos operacionales. Además de en su función de seguridad, ambos sistemas se han probado en otras aplicaciones, como la detección remota de señales vitales. Esta tésis se ha llevado a cabo completamente en el Grupo de Microondas y Radar de la Universidad Politécnica de Madrid, y se divide en dos partes principales. La primera mitad trata sobre las mejoras realizadas al sistema de imagen a 300 GHz, incluyendo una arquitectura de procesado de señal e imagen en tiempo real y las pruebas iniciales de una configuración multiestática. La segunda mitad se centra en el desarrollo del sistema de imagen hı́brido a 122 GHz y 94 GHz, detallando las caracterizaciones individuales de los sensores, su rendimiento simultáneo, y una técnica de fusión de imágenes para presentar los datos capturados. En cada sección, se estudian y detallan acciones de mejora pendientes para futuro trabajo e investigación.. Palabras Clave: Detección de amenazas ocultas, radar de imagen, ondas milimétricas, ondas submilimétricas, terahercios..

(12)

(13) Acknowledgements Quisiera expresar mi agradecimiento a todas las personas que a través de su ayuda han hecho posible esta tesis. En primer lugar, debo agradecer a mi tutor, D. Jesús Grajal de la Fuente, su apoyo durante la duración del trabajo aquı́ presentado y el haberme dado la oportunidad de llevar a cabo esta tesis. Extiendo ese agradecimiento a todo el Grupo de Microondas y Radar de la Universidad Politécnica de Madrid. En especial a mis compañeros durante estos años: Dani, Luis, Carlos, Gorka, Mario, Rodrigo, Alejandro, Luis Alberto, Marta, Clara y Carlos, en quienes me he apoyado durante este tiempo. También debo mi gratitud al Ministerio de Educación, Cultura y Deporte, por la concesión de la Beca de Formación de Profesorado Universitario, cuya aportación económica me ha permitido realizar esta tesis. También agradezco su ayuda a la Dirección General de Tráfico, que a través de su programa de ayuda a proyectos de investigación financió mi primer año como doctorando, y a NVIDIA, ya que gracias a su research grant obtuvimos nuevos recursos de computación. Además, debo agradecer a todos los proyectos nacionales e internacionales que han costeado las diversas publicaciones y conferencias realizadas durante esta tesis. Como no podı́a ser de otra manera, un agradecimiento especial merece la paciencia, el ánimo, y la compresión recibidos de mis padres y mi hermana, quienes han vivido esta tesis casi tanto como yo. Y en especialmente gracias a Ana, para quien todas las páginas de esta tesis quedarı́an cortas en agradecimiento..

(14)

(15) CONTENTS. i. Contents 1 Introduction 1.1 Overview of the Millimeter-wave Spectrum . . . . . . . 1.1.1 History of the Millimeter-wave Spectrum . . . . 1.1.2 Phenomenology of the Millimeter-wave Spectrum 1.1.3 Applications in the Millimeter-wave Spectrum . . 1.1.4 Outlook and Trends . . . . . . . . . . . . . . . . 1.2 Imaging in the Millimeter-wave Spectrum . . . . . . . . 1.2.1 Comparison between Active and Passive Imaging 1.2.2 Imager Architecture . . . . . . . . . . . . . . . . 1.2.3 State-of-the-art Imaging Systems . . . . . . . . . 1.3 Thesis Motivation and Objectives . . . . . . . . . . . . . 1.4 Publications and Author’s Contributions . . . . . . . . . 1.5 Research Projects Involved . . . . . . . . . . . . . . . . 1.6 Thesis Structure . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. 1 1 1 2 5 7 8 8 12 20 22 23 24 25. 2 300 GHz Single-sensor Security Imager 2.1 Introduction to a 300 GHz Standoff Imaging Radar . . . . . . . 2.1.1 System Description . . . . . . . . . . . . . . . . . . . . . 2.2 GPU-based Real-time Image Processing . . . . . . . . . . . . . 2.2.1 Initial Processing Architecture . . . . . . . . . . . . . . 2.2.1.1 GPU-code Profiling . . . . . . . . . . . . . . . 2.2.1.2 CPU-code Profiling . . . . . . . . . . . . . . . 2.2.1.3 Complete Profling . . . . . . . . . . . . . . . . 2.2.2 Improved Processing Architecture . . . . . . . . . . . . 2.2.2.1 GPU-code Profiling . . . . . . . . . . . . . . . 2.2.2.2 CPU-code Profiling . . . . . . . . . . . . . . . 2.2.2.3 Complete Profiling . . . . . . . . . . . . . . . . 2.2.3 System Performance . . . . . . . . . . . . . . . . . . . . 2.3 Millimeter-wave Scattering and Multistatic Configuration . . . 2.3.1 Millimeter-wave Scattering Behavior . . . . . . . . . . . 2.3.2 Imaging Simulations . . . . . . . . . . . . . . . . . . . . 2.3.3 Evaluation of Multistatic Imaging for Security Screening 2.4 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 Real-Time Imaging . . . . . . . . . . . . . . . . . . . . . 2.4.1.1 GPU-based 3-D Reconstruction . . . . . . . . 2.4.1.2 Multi-GPU Processing Architecture . . . . . . 2.4.1.3 Hardware Optimizations . . . . . . . . . . . . 2.4.2 Multistatic Scattering and Imaging . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. 27 27 28 33 36 43 48 51 51 53 56 57 59 64 67 78 82 85 85 85 86 88 89. 3 122 GHz and 94 GHz Multi-sensor Security Imager 3.1 122 GHz Radar Characterization and Performance . . . . . . . . . . . . . . . 3.1.1 122 GHz MMIC-based Radar . . . . . . . . . . . . . . . . . . . . . . . 3.1.2 Evaluation of Radar Performance . . . . . . . . . . . . . . . . . . . . .. 91 91 91 96. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . ..

(16) ii. CONTENTS. . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . .. 97 101 104 113 114 116 119 121 131 131 132 134 136 136 141 144 148 148 150 154. 4 Automatic Threat Detection (ATD) 4.1 ATD in Active Millimeter-wave Images . . . . . . . . . . . . . . . . . . 4.1.1 Image Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.2 Threat Detection with a Convolutional Neural Network (CNN) 4.1.3 Detection Results . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 ATD in Passive Infrared Images . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Image Dataset and Network Architecture . . . . . . . . . . . . 4.2.2 Detection Results . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 ATD in Active Images . . . . . . . . . . . . . . . . . . . . . . . 4.3.2 ATD in Passive Images . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. 157 157 158 162 170 182 182 186 193 194 195. 5 Other Applications 5.1 Data Acquisition on Plastic Guides . . . . . . . . . . . . 5.1.1 Radar Performance . . . . . . . . . . . . . . . . . 5.1.2 Guide Measurements . . . . . . . . . . . . . . . . 5.2 Vital Signs Monitoring . . . . . . . . . . . . . . . . . . . 5.2.1 For the 122 GHz radar . . . . . . . . . . . . . . . 5.2.2 For the 300 GHz radar . . . . . . . . . . . . . . . 5.3 Future Work . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 Data Acquisition . . . . . . . . . . . . . . . . . . 5.3.2 Vital Sign Monitoring . . . . . . . . . . . . . . . 5.3.3 Future Applications for Millimeter-wave Radars . 5.3.3.1 Vibration Detection . . . . . . . . . . . 5.3.3.2 Multi-node Sensor Network . . . . . . .. . . . . . . . . . . . .. . . . . . . . . . . . .. . . . . . . . . . . . .. . . . . . . . . . . . .. 197 197 204 210 216 219 223 225 225 227 229 229 232. 3.2. 3.3. 3.1.2.1 SNR . . . . . . . . . . . . . . . . . . . . . . . 3.1.2.2 Resolution . . . . . . . . . . . . . . . . . . . 3.1.2.3 Ranging Accuracy and Sensor Stability . . . 3.1.3 122 GHz Waveforms Considered . . . . . . . . . . . . Development of a Hybrid Imaging System . . . . . . . . . . . 3.2.1 Initial Imager and Passive Receiver Characterization . 3.2.1.1 Temperature Resolution . . . . . . . . . . . . 3.2.1.2 Image SNR and Contrast . . . . . . . . . . . 3.2.1.3 Cross-Range Resolution . . . . . . . . . . . . 3.2.1.4 Spot Size . . . . . . . . . . . . . . . . . . . . 3.2.2 Characterization of the Hybrid Imager’s Active Sensor 3.2.2.1 Cross-Range Resolution . . . . . . . . . . . . 3.2.2.2 Spot Size . . . . . . . . . . . . . . . . . . . . 3.2.3 Sensor Compatibility for Simultaneous Scanning . . . 3.2.4 Image Fusion . . . . . . . . . . . . . . . . . . . . . . . 3.2.4.1 Additional Active Sensor Image Processing . 3.2.4.2 Additional Passive Sensor Image Processing . 3.2.4.3 Imaging Scenarios . . . . . . . . . . . . . . . 3.2.5 Array Scanning . . . . . . . . . . . . . . . . . . . . . . Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . .. . . . . . . . . . . . .. . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . .. . . . . . . . . . . . .. 6 Conclusions and Future Work 235 6.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236 Bibliography. 237. Annexes. 265.

(17) CONTENTS. A Reference Data for a 300 GHz Imaging Radar A.1 Image Representation Considerations . . . . . . A.2 Automatic Target Recognition and Clustering . A.3 Image Database and Quality Metric . . . . . . A.4 Minor Changes to the Original System . . . . .. iii. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. 267 267 274 279 281. B RF Components for a 300 GHz Imaging Radar 285 B.1 x6 Active Frequency Multiplier . . . . . . . . . . . . . . . . . . . . . . . . . . 285 B.2 Bandpass Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292 C Synchronization Electronics for a 300 GHz Imaging Radar 295 C.1 Scanning Electronics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 C.2 Bandpass Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305 D Modifications to a 122 GHz Commercial Radar. 309. E Equipment and Material for Data Acquisition. 317. F Material Analysis with FMCW Radars 325 F.1 Analysis of Absorption in Plastics . . . . . . . . . . . . . . . . . . . . . . . . 325 F.2 Analysis of Absorption in Common Fabrics . . . . . . . . . . . . . . . . . . . 333 F.3 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336 G Bandwidth and Size Improvements for the 300 GHz Imaging Radar. 339. Acronyms. 345.

(18) iv. CONTENTS.

(19) LIST OF FIGURES. v. List of Figures 1.1 1.2 1.3. 1.4 1.5. 1.6. 1.7 1.8 1.9. 1.10 1.11. 1.12. 1.13 2.1 2.2 2.3. Region of the electromagnetic spectrum which includes the mm-wave, submmwave, and THz bands. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Absorption coefficient of deionized water according to the signal’s frequency, reproduced from [16]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Atmospheric attenuation throughout the spectrum, with marked absorption peaks from oxygen and water molecules, reproduced from [19]. Above ∼300 GHz windows become less broad as closer absorption lines appear and baseline attenuation increases. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Absorption of various fabrics throughout the THz spectrum, modified from [23]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (a) Active imaging scenario, where received power is dominated by reflections of the transmitted signal. (b) Passive imaging scenario, where received power presents approximately equally-powerful contributions from background, clutter, and the subject’s own emissions. . . . . . . . . . . . . . . . . . . . . . . . (a) Passive outdoors imaging scenario, where received temperature from threat and subject presents a good contrast thanks to the sky’s cold radiation. (b) Passive indoors imaging scenario, where received temperature from threat and subject presents a low contrast due to the room’s warm surroundings. . . . . Block diagram of a generic single-antenna FMCW architecture. . . . . . . . . Time-frequency diagram of sawtooth FMCW operation. . . . . . . . . . . . . (a) Beat frequency as a function of the range to the target and chirp time, when the transmitted bandwidth is 10 GHz. (b) Beat frequency as a function of the range to the target and bandwidth, when the chirp time is 1 ms. . . . (a) Range resolution as a function of the transmitted bandwidth, when α = 1. (b) Necessary bandwidth for a range resolution of 1 cm when α > 1. . . . (a) Handheld W-band sensor, reproduced from [100]. (b) The commercial ProVision2 portal imager from L3 Security & Detection Systems, reproduced from [101]. (c) 300 GHz standoff imager, reproduced from [102]. . . . . . . . (a) Single TRX scanning where the reflector must be mechanically moved to illuminate each spot. (b) Multi-TRX array scanning where beamforming techniques are used to illuminate each spot without need for movement. (c) Combination of mechanical and array scanning, where the TRXs in the array simultaneously scan a full row of the spot grid while a mechanical movement of the reflector provides coverage for the vertical dimension. . . . . . . . . . . (a) Vertical raster scanning. (b) Diagonal scanning. (c) Elliptical scanning. .. 1 3. 3 4. 10. 11 13 15. 15 16. 17. 19 20. Photograph of the TX and RX mm-wave chains of the 300 GHz imaging radar. 29 Spectral comparison of a focused and unfocused metallic sphere with the radar using the BEGRS reflector system. . . . . . . . . . . . . . . . . . . . . . . . . 30 Photograph detailing the reflector subsystem, diagram of the mirror’s movement, and representation of the scan pattern over a human target, with the axes used throughout the text. The z axis goes through the torso, from front to back. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.

(20) vi. LIST OF FIGURES. 2.4 2.5 2.6 2.7. 2.8 2.9 2.10 2.11. 2.12. 2.13. 2.14. 2.15. 2.16. 2.17. 2.18 2.19. 2.20. Block diagram of the 300 GHz imaging radar, divided into its main subsystems. CUDA thread (left) and memory (right) organization. Reproduced from [143] Main stages of the initial signal processing algorithm, in execution order from left to right, and top to bottom. . . . . . . . . . . . . . . . . . . . . . . . . . Scan pattern of the GMR’s imaging radar and sample results from some of the signal processing events in the algorithm. Notice that the x-axis in (c) has already been labeled according to R instead of frequency, through the relationship seen in (1.18). The final product is a 3-D point cloud of the internal layer of a female mannequin wearing a T-shirt and hiding a simulated explosive device in the abdomen area. Colorbar: Range in meters (R-axis). . Range gating (R-axis) in a target’s silhouette. . . . . . . . . . . . . . . . . . . Flow diagram of the peak detection algorithm for extracting range layers from a chirp’s spectral samples. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Diagram of the elevation and rotation axes, as well as angles, in the radar’s scanning mirror. Reproduced from [125]. . . . . . . . . . . . . . . . . . . . . . Sample point cloud and reconstructed images of the internal layer of a female mannequin wearing a T-shirt and hiding a handgun in the abdomen area. Colorbar: Range in meters (R-axis). . . . . . . . . . . . . . . . . . . . . . . . Relationship between data and thread structures in the parallel algorithm for a sample image. Each point cloud spot represents one LFM ramp, and is assigned to one GPU thread. Thread blocks are formed by grouping these spots in sub-images, as seen in the sample dotted squares. . . . . . . . . . . . Sequence of events and transfers, in chronological order from left to right, from the reception of the beat frequency until the display of its image. The legend details the connections in each stage, and the color code separates hardware platforms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Timeline of synchronization between scanning, image processing, and display in a sample scan. In this example, image refresh is limited by the mechanical scanning; processing can be contiguous and display can be faster, if the ADC data of all images are available sooner on the GPU. . . . . . . . . . . . . . . Average GPU-based signal processing rate per 6000-LFM ramp image for various ADC data sizes. Configurations vary depending on signal processing and display choices seen in Table 2.4. Each configuration time is averaged over 100 images, per datapoint in the x-axis. (a) Shows the required GPU processing time per image. (b) Shows the processing speed in images per second [the inverse of (a)]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . GPU processing time per image for all kernels, averaged over 100 images. Only one peak detection algorithm is chosen per image. FFTs are done in batched mode with CUDA’s CUFFT library, while all other kernels are custom-made. Profiling is done with NVIDIA’s Visual Profiler. . . . . . . . . . . . . . . . . GPU resource use for all kernels, averaged over 100 images. Only one peak detection algorithm is chosen per image. FFTs are done in batched mode with CUDA’s CUFFT library, while all other kernels are custom-made. Profiling is done with NVIDIA’s Visual Profiler. . . . . . . . . . . . . . . . . . . . . . Point cloud image of a mannequin with a simulated explosive device in the abdomen area, in VTK (left) and MATLAB (right). . . . . . . . . . . . . . . Multi-thread image refresh structure using VTK. Separate CPU threads are assigned to data transfers and image display in order to enable real-time scanning and display. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Histogram of recorded display times for images, measured over 100 images, with varying number of CPU threads available. . . . . . . . . . . . . . . . . .. 32 35 36. 37 38 39 40. 41. 41. 42. 42. 43. 44. 48 49. 49 50.

(21) LIST OF FIGURES. 2.21. 2.22. 2.23. 2.24. 2.25 2.26. 2.27. 2.28. 2.29. 2.30. 2.31. 2.32 2.33. In black, main stages of the improved signal processing algorithm, in execution order from left to right. In red, stages removed from the previous architecture in Fig. 2.6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Average GPU-based signal processing rate per 6000-LFM-ramp image for various ADC data sizes. Configurations vary depending on the signal processing and display choices seen in Table 2.7. Each configuration time is averaged over 100 images, per datapoint in the x-axis. (a) Shows the required GPU processing time per image. (b) Shows the processing speed in images per second [the inverse of (a)]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . GPU processing time per image for the remaining kernels, averaged over 100 images. Only one peak detection algorithm is chosen per image. FFTs are done in batched mode with CUDA’s CUFFT library, while all other kernels are custom-made. Profiling is done with NVIDIA’s Visual Profiler. . . . . . . GPU resource use for all kernels, averaged over 100 images. Only one peak detection algorithm is chosen per image. FFTs are done in batched mode with CUDA’s CUFFT library, while all other kernels are custom-made. Profiling is done with NVIDIA’s Visual Profiler. . . . . . . . . . . . . . . . . . . . . . Histogram of recorded display times for images, measured over 100 images, with varying number of CPU threads available. . . . . . . . . . . . . . . . . . Possible timeline of image processing and display, with parallelized GPU processing and host-to-device memory transfers with the use of two different CUDA streams. ADC data samples of all images must be at least synchronized with GPU transfers and at least 6 CPU threads are necessary. . . . . . . . . Photograph of (a) the real-time scanning set-up for a static metallic object, and (b) the background target used (a rectangular metal frame holding a thin bar with a sphere attached at one end). . . . . . . . . . . . . . . . . . . . . . Sample images from a continuous video sequence at ∼1 fps of a male entering and exiting the FoV (while extending and closing his arms), as captured by the radar. The static metal frame from Fig. 2.27(b) is present in the background. The chronological sequence starts from the top left corner, left to right, and is also indicated by the numerical order attached to each image. Colorbar: Received power in dBm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sample images from a continuous video sequence at ∼1 fps of a male entering and exiting the FoV (while crossing his arms in front), as captured by the radar. The static metal frame from Fig. 2.27(b) is present in the background. The chronological sequence starts from the top left corner, left to right, and is also indicated by the numerical order attached to each image. Colorbar: Range in meters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sample images from a continuous video sequence at ∼2 fps of a male entering the FoV (with his arms extended), as captured by the radar. The static metal frame from Fig. 2.27(b) is present in the background. The chronological sequence starts from the top left corner, left to right, and is also indicated by the numerical order attached to each image. Colorbar: Range in meters. . . . Illustrations of different imaging architectures according to the number of radiating elements and their placement. (a) Shows a standoff monostatic case, (b) shows walkthrough panel arrays, and (c) shows a standoff multistatic scenario. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (a) Specular reflection from a smooth surface, where θi = θr (b) Diffuse reflection from a rough surface, where ∆θr contains various reflection angles. . Standoff multi-bistatic imaging configuration, simplified from the multistatic architecture in Fig. 2.31(d). . . . . . . . . . . . . . . . . . . . . . . . . . . . .. vii. 52. 53. 54. 55 56. 58. 60. 61. 62. 63. 64 65 66.

(22) viii. 2.34. 2.35 2.36 2.37 2.38. 2.39. 2.40. 2.41. 2.42. 2.43. 2.44. 2.45. 2.46. 2.47. LIST OF FIGURES. Block diagram of the implemented multi-bistatic architecture using an independent TX. To generate mulitple bistatic images, the TX can be moved throughout the scene. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Separation of the 300 GHz TX chain from the reflector system with a photography tripod for multistatic measurements. . . . . . . . . . . . . . . . . . . . (a) Bistatic FMCW radar configuration with one TRX and one TX. (b) Sawtooth FMCW signals in the bistatic configuration. . . . . . . . . . . . . . . . Diagram of the processing pipeline to obtain and combine simultaneous monostatic and bistatic point clouds. Colorbars: Range in meters. . . . . . . . . . (a) Measurement target consisting of a smooth metallic sheet held with a rotational positioner. (b) EPS foam slab in the same positioner. (c) Returned power from both targets in a monostatic TRX, and from TX placements at θ = 6◦ and θ = 12◦ , as the target is being rotated around its vertical axis. (d) Zoomed-in view of the EPS results, with the metallic data removed for better visibility. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Monostatic images of a smooth metallic sheet (a) oriented towards the TRX, (b) rotated 3◦ , and (c) rotated 6◦ . Monostatic images of a EPS block (d) oriented towards the TRX, (e) rotated 3◦ , and (f) rotated 6◦ . Colorbar: Returned power in dBm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (a) Measurement target consisting of a replica Colt 1911 handgun held with a rotational positioner. (b) Imaging result when the target is oriented towards the radar. (c) Imaging result when the target is rotated just 3◦ . (d) Bistatic image of the handgun when radiated with a TX with θ = 6◦ of angular diversity. (e) Multistatic image, combined from the previous monostatic and bistatic results. Colorbar: Received power in dBm. . . . . . . . . . . . . . . . (a) Matrix of 2.5-mm-radius metallic spheres. (b) Monostatic image. (c) Bistatic image of θ = 3◦ angular diversity. (d) Bistatic image of θ = 6◦ angular diversity. Colorbar: Received power in dBm. The total range of the colormaps is 9 dB in all images, but not set to common limits to account for different transmit power in bistatic measurements. . . . . . . . . . . . . . . . (a) Nude female mannequin. (b) Monostatic image when the mannequin is facing the reflector system. (c) Multistatic combination of all monostatic and bistatic images when the TX is placed at four locations achieving θ = ±3◦ and θ = ±6◦ bistatic angles. Colorbar: Range in meters. . . . . . . . . . . . . Intermediate combinations from the imaging scenario in Fig. 2.42. (a) Monostatic only. (b) Monostatic + Bistatic at θ = −3◦ . (c) Monostatic + Bistatic at θ = ±3◦ . (d) Monostatic + Bistatic at θ = ±3◦ and at θ = −6◦ . (e) Monostatic + Bistatic at θ = ±3◦ and θ = ±6◦ Colorbar: Range in meters. . (a) Structured-light point cloud of a nude female mannequin. Colorbar: Range in meters. (b) Registered radar and structured-light point clouds of a nude female mannequin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (a) Monostatic ray-tracing simulation result when a metallic sheet is oriented towards the radar. (b) Monostatic ray-tracing simulation result when a metallic sheet is rotated just 3◦ . Colorbar: Received power in dBm. . . . . . . . . (a) 3-D model of a MP-443 handgun used as simulation target. (b) Monostatic ray-tracing simulation result when the handgun is oriented towards the radar. (c) Monostatic ray-tracing simulation result when the handgun is rotated just 3◦ . Colorbar: Received power in dBm. . . . . . . . . . . . . . . . . . . . . . . Comparison between simulated and measured returned power from a smooth metallic sheet in monostatic and bistatic (at θ = 6◦ and θ = 12◦ angular diversities) placements, as the sheet is being rotated around its vertical axis. Measured data comes from Fig. 2.38. . . . . . . . . . . . . . . . . . . . . . . .. 66 67 68 69. 70. 72. 73. 74. 75. 76. 77. 80. 80. 81.

(23) LIST OF FIGURES. 2.48. (a) Simulated monostatic imaging result equivalent to Fig. 2.40(b). Simulated monostatic imaging result equivalent to Fig. 2.40(c) (a 3◦ rotation). (c) Simulated bistatic imaging result equivalent to Fig. 2.40(d) (a 3◦ rotation). Colorbar: Normalized received power in dB. . . . . . . . . . . . . . . . . . . .. ix. 81. 2.49. (a) Simulated monostatic image equivalent to Fig. 2.42(b). (c) Simulated multistatic combination equivalent to Fig. 2.42(c). Colorbar: Range in meters. 82. 2.50. (a) Female mannequin wearing a T-shirt to hide a ceramic knife near the abdomen. (b) Placement of the knife underneath the T-shirt. (c) Monostatic image when the mannequin is facing the reflector system. (d) Monostatic image when the mannequin is rotated 10◦ counterclockwise. (e) Combined image of the mannequin from the bistatic result when radiated with a TX with θ = 20◦ of angular diversity and the monostatic result in (d). Colorbar: Range in meters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 84. 2.51. (a) Female mannequin concealing the gun from Fig. 2.40 with a T-shirt. (b) Photograph of the gun placement near the abdomen. (c) Monostatic image when the mannequin is facing the reflector system. (d) Monostatic image when the mannequin is rotated 10◦ counterclockwise. (e) Combined image of the mannequin from the bistatic result when radiated with a TX with θ = 20◦ of angular diversity and the monostatic result in (d). Colorbar: Range in meters. 84. 2.52. (a) Male human wearing a sweater to hide the handgun from Fig. 2.40(a) near the abdomen. (b) Monostatic image when the human is facing the reflector system. (c) Monostatic image when the human is rotated 10◦ counterclockwise. (d) Combined multistatic image of the male human from the bistatic results when radiated with TXs with θ = 15◦ , θ = 20◦ , and θ = 25◦ angular diversities and the monostatic result in (c). Colorbar: Range in meters. . . .. 85. 2.53. Histograms of the registered processing times over 1000 iterations for the 3-D Delaunay triangulation of two radar images, when performed on the GTX 970 using gStar4D. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 86. Photographs of four interconnected NVIDIA TITAN GPUs (a) and two AMD Radeon GPUs (b). The NVIDIA models are connected with an SLI bridge, while the AMD devices are connected with a CrossFire bridge. . . . . . . . .. 87. FoV scan patterns for a (a) high-quality image (8000 rpm, 827 steps/s, 150 µs) per chirp), (b) the fastest scan currently available (8000 rpm, 1600 steps/s, 150 µs per chirp), (c) a modified maximum (16000 rpm, 1800 steps/s, 75 µs per chirp), (d)-(f) and their corresponding imaging results for a nude female mannequin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 89. Photographs of the commercial FMCW radar kit from Silicon Radar GmbH (a) without and (b) with its dielectric lens attached. Modified from [194]. . .. 92. (a) Block diagram of the architecture of the modified 122 GHz radar from a Silicon Radar GmbH commercial kit, and (b) photograph detailing its components. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 94. Photographs of the (a) TRA 120 01 and (b) TRA 120 012 MMICs from Silicon Radar GmbH, reproduced from [194]. . . . . . . . . . . . . . . . . . . . .. 95. SNR analysis using the MMIC-based sensor at 122 GHz for a 70 dBsm corner reflector, according to (a) (1.23) and (b) (1.24). . . . . . . . . . . . . . . . . .. 97. (a) Scaling of the SNR of a metallic corner reflector with a peak RCS of 70 dBsm as it is placed at increasing distances from the radar. Sweep time is 150 µs, and 15.1 GHz of bandwidth are transmitted. (b) Photograph of the corner reflector used. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 98. 2.54. 2.55. 3.1 3.2. 3.3 3.4 3.5.

(24) x. LIST OF FIGURES. 3.6. 3.7. 3.8. 3.9 3.10 3.11 3.12. 3.13. 3.14. 3.15. 3.16. 3.17. 3.18. 3.19. 3.20. Noise floor measurements when the external ADC is connected to (a) a 50 Ω load and (b) the developed radar aiming at a milimeter-wave absorber. Varying chirp times are considered and used during the FFTs that produce the represented spectrums. . . . . . . . . . . . . . . . . . . . . . . . . . . . . SNR analysis using the MMIC-based sensor at 122 GHz for a collection of common imaging objects, according to (1.23), when the dwell time is (a) 75 µs, (b) 150 µs, and (c) 1500 µs. . . . . . . . . . . . . . . . . . . . . . . . . . . (a) Sensitivity measurement and noise floor comparisons, (b) and zoom, for a metallic sphere with a 2 cm diameter illuminated by the 122 GHz radar with a 6.3 GHz bandwidth and a 150 µs chirp. (c) Photograph of the measurement set-up. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fraunhofer distance according to frequencies delimiting a 18 GHz bandwidth around the developed radar’s 122 GHz center frequency. . . . . . . . . . . . . Spot size estimations for the MMIC-based sensor at 122 GHz according to (3.5), with (a) and without (b) using the focusing lens. . . . . . . . . . . . . . Transmitted bandwidth measurement by placing a high-frequency probe in the feedback loop of the front end’s PLL. . . . . . . . . . . . . . . . . . . . . Maximum available bandwidth measurement for the (a) TRA 120 01 and (b) TRA 120 012 MMICs. To perform the measurement a probe is placed at the divided signal path of the PLL loop, and the frequency axis is converted to the RF range. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Range resolution measurement for the (a) TRA 120 01 and (b) TRA 120 012 MMICs. To perform the measurement two targets (one cardboard and one metal) are placed facing the radar, one in front of the other, and their spectrums captured. The resolution is defined as the minimum distance that still ensures a 6 dB null between targets in the spectrum. . . . . . . . . . . . . . . Measurement of the radar’s deviation from (1.18) in its beat frequency to range relationship. A 70 dBsm corner reflector is placed at increasing distances and its beat frequency is registered and compared against the theoretical value from (1.18), for the (a) TRA 120 01 and (b) TRA 120 012 MMICs. . . . . . Measurement of the radar’s ranging accuracy. A 70 dBsm corner reflector is placed at increasing distances and its measured range according to (1.18) is compared against the actual distance to the radar, for the (a) TRA 120 01 and (b) TRA 120 012 MMICs. . . . . . . . . . . . . . . . . . . . . . . . . . . Measurement of the radar’s range-measuring accuracy when two targets in the same line of sight are present in the scene. The first target is placed at 2 m, while the second target progressively moves farther away. For the error calculation, the measured distance between targets is compared against the actual distance, for the (a) TRA 120 01 and (b) TRA 120 012 MMICs. . . . Distance error for different phase fluctuations and frequencies, according to (3.6). For each frequency and fluctuation combination, target movements smaller than the corresponding curve cannot be measured accurately. . . . . (a) Photograph of the positioning stage and metallic plate. (b) Distance error from phase variation, according to (3.6) for the static metallic plate during a 3 second capture using 150 µs chirps. (c) Histogram of the distance error. . . Movement sequence with the XY stage, as captured by the radar with 150 µs chirps. In (a) the plate is moved back 10 µm, the minimum resolution of the stage. In (b) the plate is moved back another 50 µm. In (c) the plate is moved 1.5 mm in approximately 17 smaller steps. . . . . . . . . . . . . . . . . . . . . (a) Distance error from phase variation, according to (3.6) for the static metallic plate during a 0.7 second capture using 600 µs chirps. (b) Histogram for the distance error. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 99. 100. 100 101 102 103. 103. 104. 105. 105. 107. 107. 108. 109. 109.

(25) LIST OF FIGURES. 3.21 3.22. 3.23. 3.24. 3.25. 3.26. 3.27 3.28. 3.29. 3.30 3.31 3.32 3.33 3.34. 3.35. 3.36. 3.37. Distance error from phase variation, according to (3.6) for the static metallic plate during 1 s captures, taken every 5 minutes, using 600 µs chirps. . . . . IR images of the 122 GHz radar’s PCB, taken every 5 minutes, as it is in continuous operation. In a span of 15 minutes the radar’s maximum temperature increases by 2 ◦ C. As can be seen, the USB connection of the microcontroller (which feeds the power consumption of the entire radar) and the MMIC are the two hottest components. Colorbar: Temperature in ◦ C. . . . . . . . . . . (a) Photograph of the temperature control set-up using PC ventilation fans. (b) Distance error from phase variation, according to (3.6) for the static metallic plate during 1 second captures using 600 µs chirps, with and without temperature control. (c) Histograms of the distance errors with and without temperature control. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparison, with and without temperature control, of returned power variations from a static metallic plate using 600 µs chirps with 6.3 GHz of bandwidth for the TRA 120 01 MMIC and 15.1 GHz of bandwidth for the TRA 120 012 MMIC. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Variations in the calibration procedure’s performance in the 300 GHz imaging radar, when aiming at a static target, as time passes. The same reference signal is used for calibration in all times. . . . . . . . . . . . . . . . . . . . . . Spectral comparison of non-calibrated, calibrated, and ideal sincs from a metallic sphere placed at 1 m, for the (a) TRA 120 01 MMIC transmitting 6.3 GHz of bandwidth in 150 µs chirps and the (b) TRA 120 012 MMIC transmitting 15.1 GHz of bandwidth in 150 µs chirps. . . . . . . . . . . . . . . . . Diagram of simultaneous active, passive, and optical scanning of a human target. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (a) Representation of the hybrid imager’s reflector system including the active, passive, and optical sensors. (b) Photograph of the side-by-side passive (gold) and active (red) sensors in front of the metallic mirror. . . . . . . . . . . . . . (a) Exterior and (b) internal photographs of Wavecamm’s passive imager, (c) and diagram of the FoV scan achieved with the combined movements of the reflector system. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ray-tracing illustration showing the vertical FoV sweep performed in a single mirror turn in four steps in chronological order, from (a) to (d). . . . . . . . (a) Passive sensor’s output when terminated with a 50 Ω load at 25◦ . (b) Histogram of the sensor’s output. . . . . . . . . . . . . . . . . . . . . . . . . . Block diagram of the PCB used to condition and sample the passive sensor’s output. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Outputs at different stages of the PCB diagrammed in Fig. 3.32 when a 0.9 V - 1.1 V sinusoid is input for testing and calibration. . . . . . . . . . . . . . Sequence of passive RX output measurements over time, as the target (a water bottle initially at 0 ◦ C) gradually heats up to room temperature (22 ◦ C). The X-axis marks the minimum temperature registered in the bottle. . . . . . . . Histograms of the passive RX output for four different scenarios (a mm-wave absorber, a human hand, a room-temperature water bottle, and a clear sky). The temperature of each target is listed as reference. . . . . . . . . . . . . . . Measurement of three different targets (mm-wave absorber, human hand, and cold water bottle), where each one has 10 datapoints in the graph. The absorber was at 30 ◦ C, the hand at 33 ◦ C, and the bottle at 20 ◦ C. . . . . . . . SNR of the passive RX throughout the sequence illustrated in Fig. 3.34. The SNR is computed as the logarithmic scale ratio of the mean signal in each measurement to its standard deviation. . . . . . . . . . . . . . . . . . . . . .. xi. 110. 110. 111. 112. 113. 113 114. 115. 116 117 118 118 119. 120. 120. 121. 122.

(26) xii. LIST OF FIGURES. 3.38. Comparison of raw passive images of a male subject under various atmospheric conditions. In (a) the target is scanned in an outdoors environment, in (b) the target is scanned indoors in uncontrolled conditions, in (c) the target is scanned indoors in uncontrolled conditions in front of an open window facing the sky, and in (d) the target is scanned indoors in controlled conditions. . . 123. 3.39. (a) IR camera snapshot of a male target wearing a loose T-shirt and hiding an IED threat (at 7 ◦ C). Colorbar: Temperature in degrees. (b) Raw output image of the passive sensor. (c) Result of thresholding and filtering the raw image with a 2-D asymmetric median kernel. (d) Final passive image, after reducing the image value interval. (e) Graph of a single column in (c), cutting through background, body, and IED. The samples corresponding to the IED can be clearly separated from the body. . . . . . . . . . . . . . . . . . . . . . 123. 3.40. (a) IR camera snapshot of a male target hiding a threat (a filled roomtemperature 1 L water bottle) underneath a thick sweatshirt and T-shirt. Colorbar: Temperature in degrees. (b) Filtered and thresholded output image of the passive sensor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124. 3.41. Measurement sequence with the passive sensor of a male subject with a rectangular target in his chest, as the room cools down from air conditioning. The sequence shows both 94 GHz and IR images. Colorbar: Temperature in degrees. The image in (a) is taken at the beginning of the sequence, with no air conditioning. In (b) the air conditioning is turned on, and images approximately every 30 minutes are taken in (c) - (f). . . . . . . . . . . . . . 125. 3.42. Measurement sequence with the passive sensor as a threat warms up, while hidden beneath a male subject’s T-shirt. The threat is the previously-used IED, made up of three individual capacitors. The left column shows IR closeups of the torso, while the center and right column are the raw and processed images obtained with the passive RX, respectively. Colorbar: Temperature in degrees. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126. 3.43. Histograms of the raw RX outputs when the sensor aims at a water bottle in various concealment scenarios, and photographs of some of the set-ups. Outputs from the unconcealed bottle and a mm-wave absorber are also included for comparison. In (a) the concealed bottle is covered with a polyester T-shirt, in (c) with a cotton sweater, and in (e) with denim jeans. The photograph in (b) shows the unconvered bottle, the one in (d) shows the mm-wave absorber, and the one in (f) shows the covered bottle (with denim). . . . . . . . . . . . 127. 3.44. Passive images of a cool water bottle in the concealment scenarios analyzed in Fig. 3.43. In (a) the bottle is uncovered, in (b) the bottle is covered with a polyester T-shirt, in (c) the bottle is covered with a cotton sweater, and in (d) the bottle is covered with denim. . . . . . . . . . . . . . . . . . . . . . . . 128. 3.45. Passive image data used for added noise testing. The subject is a male wearing a T-shirt hiding a cool water bottle in the abdomen. (a) Raw samples of the passive sensor’s output in digital values, (b) histogram of raw and filtered samples, (c) raw image, and (c) filtered image. . . . . . . . . . . . . . . . . . 129. 3.46. Passive image data from Fig. 3.45 with additional AWGN. The left column are histograms of the additional AWGN introduced in each row, the center column is the corresponding raw image, while the right column is the filtered image. The top row is a scenario where the AWGN has a standard deviation equal to 50% of the standard deviation of the data in Fig. 3.45(a), the middle row is a scenario where the AWGN is raised to 100% of the original standard deviation, and the bottom row is a scenario where the AWGN is 150% of the original standard deviation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130.

(27) LIST OF FIGURES. 3.47. 3.48. 3.49. 3.50. 3.51. 3.52. 3.53. 3.54. 3.55. 3.56 3.57. 3.58. 3.59 3.60. (a) Measurement of the cross-range resolution of the passive sensor along the horizontal dimension using the reflector system. 2 cm-diameter frozen plastic spheres are placed 5 cm apart, and sequentially brought together in front of a human. The distances above the images represent the separation between the objects’ centers. (b) Same sequence as above, for the vertical dimension. . . . Measurement of the spot size of the passive sensor along the horizontal (X) and vertical (Y) dimensions using the reflector system. A 2 cm-diameter frozen plastic sphere is illuminated, and single line scans in each dimension are measured. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (a) Photograph of the male subject (the location of the threat underneath the T-shirt is indicated). (b) Scans of the set-up on the left, using the modified active sensor in the reflector system. In the examples on the top the person is hiding an IED composed of three cylindrical capacitors, while on the bottom figures no threat is hidden. Examples on the left are representations in 3-D point cloud format while those on the right depict a surface reconstruction of the point clouds. A photograph of the hidden threat is inset between the examples. Colorbar: Range in meters. . . . . . . . . . . . . . . . . . . . . . . Noise power level as a function of the maximum reflected power per pixel for all pixels obtained from Fig. 3.49(b) (with the IED). The red data uses a single range bin near the target to estimate noise power, while the blue data uses the mean power of 50 range bins around the target. . . . . . . . . . . . . . . Measurement of the cross-range resolution of the active sensor using the reflector system. The top row is the resolution analysis in the horizontal dimension, and the bottom row in the vertical dimension. The centers of two metallic spheres are placed at a varying distance [(a) and (d) 4 cm, (b) and (e) 3 cm, (c) and (f) 2 cm]. Colorbar: Received power in dBm. . . . . . . . . . . . . . . Measurement of the cross-range resolution of the active sensor along the horizontal and vertical dimensions using the reflector system. 5 mm-diameter metallic spheres are placed 7.5 cm apart, in both directions, over a polystyrene sheet. Colorbar: Received power in dBm. . . . . . . . . . . . . . . . . . . . . Measurement of the spot size of the active sensor along the horizontal (X) and vertical (Y) dimensions using the reflector system. A 5 mm-diameter metallic sphere is radiated, and single line scans in each dimension are measured. . . . Interference in the passive sensor’s output caused by the active sensor. (a) Measurement set-up, and passive sensor output comparisons in the (b) time and (c) frequency domains. . . . . . . . . . . . . . . . . . . . . . . . . . . . . Passive images of a male subject with no hidden threat. (a) Active sensor turned off, (b) active sensor transmitting chirp (interference boxed in red), and (c) comparison of the passive sensor’s raw output in both cases. . . . . . Dependence of the interference in the passive sensor with the active sensor’s transmitted bandwidth. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Imaging example when the active sensor is off-center in the reflector system. (a) Passive image with interference shifted slightly away from the subject’s abdomen (interference boxed in red), and (b) corresponding active point cloud. Colorbar: Range in meters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . Passive and active images of a male subject with no hidden threat when the active sensor transmits pulsed chirps in a 1-to-10 ratio. (a) Raw passive image, (b) raw active image, and (c) interpolated and filtered active image. Colorbar: Returned power in dBm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Passive sensor outputs under different active sensor transmissions. (a) Output in the time domain, and (b) histograms of those outputs. . . . . . . . . . . . Sample combination of active sensor (a) and Kinect (b) outputs via an overlay (c) of their two images with 50% transparency. . . . . . . . . . . . . . . . . .. xiii. 131. 132. 133. 133. 134. 135. 136. 137. 138 139. 139. 140 141 141.

(28) xiv. LIST OF FIGURES. 3.61. Diagram of the image combination process of all three sensors involved in the imager. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142. 3.62. (a) Photograph from the Kinect’s RGB camera, with face blurring applied and the detected skeleton represented as red segments. (b) Output of the Kinect’s IR sensor with a grayscale colormap representing depth. (c) Initial depth point cloud with RGB texture from the camera. (d) Final Kinect 3-D point cloud, later used as depth mask, after segmenting the initial point cloud within the subject’s range window. . . . . . . . . . . . . . . . . . . . . . . . . 142. 3.63. Breakdown of the processing steps for fusing two images using the DWTbased technique proposed. The unfused input images are from the Kinect and active sensor, for the imaging scenario in Fig. 3.49. Decompositions that include passing through the high-pass filter are reduced to simply the borders of the input image, because these boundaries produce a sharp change in image data (interpreted as a high-frequency component). This is also why the top decomposition, created by passing only through the low-pass filter, is approximately the same image as the input; values inside the image’s borders have very smooth changes (interpreted as low-frequency information). The Kinect’s depth data has been used to generate image I1 (x, y), but the colored range information in the final fused image [Z(x, y)] comes from the active sensor. . 145. 3.64. Diagram of the complete processing chain and outputs at different stages. Blocks in different colors are processed in separate CPU threads. . . . . . . . 146. 3.65. (a) Imaging scenario of male target hiding a room-temperature IED of three cylindrical capacitors underneath a T-shirt in the abdomen area. In this case both sensors expose the threat. (b) Range-based representation of the active sensor data. (c) Active sensor data fused with the Kinect point cloud. Colorbar: Range in meters. (d) Passive sensor data. (e) Passive sensor data fused with the Kinect point cloud. . . . . . . . . . . . . . . . . . . . . . . . . . . . 147. 3.66. (a) Range point cloud of the active sensor. Colormap: Range in meters. (b) Registration of the range point cloud with the Kinect’s point cloud. (c) Filtered grid with range data. (d) Fused range data and Kinect mask. (e) Returned power point cloud of the active sensor. Colormap: Returned power in dBm. (f) Registration of the returned power point cloud with the Kinect’s point cloud. (g) Filtered grid with returned power data. (h) Fused returned power data and Kinect mask. . . . . . . . . . . . . . . . . . . . . . . . . . . . 147. 3.67. (a) Thresholded and filtered output of the passive sensor. (b) Cropped passive image, where the red region corresponds to the result of performing a colored flood-fill of holes inside the torso. (c) Fused passive data and Kinect mask. . 148. 3.68. (a) Imaging scenario of male target hiding a room-temperature pocketknife in the left pocket of his jeans. (b) Range-based representation of the active sensor data. (c) Active sensor data fused with the Kinect point cloud. Colorbar: Range in meters. (d) Passive sensor data. (e) Passive sensor data fused with the Kinect point cloud. This is the only representation in this scenario where the threat is exposed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149. 3.69. (a) Imaging scenario of male target hiding a simulated plastic explosive with shrapnel, with has warmed up to body-temperature, in the right side of his abdomen. (b) Range-based representation of the active sensor data. (c) Active sensor data fused with the Kinect point cloud. Colorbar: Range in meters. This is the only representation in this scenario where the threat is exposed. (d) Passive sensor data. (e) Passive sensor data fused with the Kinect point cloud. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149.

(29) LIST OF FIGURES. 3.70. 3.71. 3.72. 3.73 3.74 3.75. 3.76. 3.77. 3.78. 4.1 4.2. 4.3 4.4 4.5 4.6 4.7. 4.8. xv. (a) Imaging scenario of male target hiding a plastic 1 L sport bottle full of room-temperature water underneath a T-shirt in his stomach area. In this case both, sensors expose the threat. (b) Range-based representation of the active sensor data. (c) Active sensor data fused with the Kinect point cloud. Colorbar: Range in meters. (d) Passive sensor data. (e) Passive sensor data fused with the Kinect point cloud. . . . . . . . . . . . . . . . . . . . . . . . . 150 (a) Diagram of array scanning with two sensors, placed on a moving platform, simultaneously transmitting at different parts of the FoV. (b) Photograph of the four passive RXs mounted in an array configuration in the reflector system.150 Multi-sensor scanning of a male subject with four passive RXs in an array configuration. (a) Joined raw outputs of the 4 sensors, (b) filtered outputs, and (c) normalized and thresholded outputs. . . . . . . . . . . . . . . . . . . 151 Histograms of the raw outputs of the four passive RXs during the imaging scenario in Fig. 3.72. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 Photographs of two 122 GHz radars placed in an array configuration for simultaneous scanning in the imager. . . . . . . . . . . . . . . . . . . . . . . . . 152 Multi-sensor scanning of a male subject with two 122 GHz radars in an array configuration. (a) Point cloud from the first sensor, (b) point cloud from the second sensor, (c) and combined point clouds. Colorbar: Range in meters. (d) Point cloud from the first sensor, (e) point cloud from the second sensor, and (f) combined point cloud. Colorbar: Returner power in dBm. . . . . . . 153 Combined multi-sensor scans of a male subject with two 122 GHz radars in an array configuration, after processing each sensor’s images with different detection thresholds. (a) Colorbar: Range in meters. (b) Colorbar: Returned power in dBm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 (a) Set-up for hybrid imaging with the 94 GHz RX mounted in the 300 GHz reflector system. (b) Interferences in the 94 GHz RX caused by the 300 GHz radar’s TRX. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 (a) Nude female mannequin used as target, (b) active image of the mannequin with the previous PCB, and (c) active image for that same scenario with the new PCB. Colorbar: Returned power in dBm. . . . . . . . . . . . . . . . . . . 155 Sample display from a mm-wave security scanner from L-3 Technologies running ATD algorithms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sample images from the DHS dataset. (a) Front view of a man with no threat. (b) Side view of a woman with no threat. (c) Side view of a man with a concealed threat near his right knee. (d) Side view of a woman with a concealed threat near her left calf. (e) Side view of a man with a concealed threat near his right ribcage. (f) Back view of man with a concealed threat near his shoulder blades. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sample 360◦ scan of a male subject in the DHS dataset. . . . . . . . . . . . . Diagram of possible threat locations for the images in the DHS dataset. Reproduced from [238]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Flow diagram of a perceptron. . . . . . . . . . . . . . . . . . . . . . . . . . . Generic diagram of a simple CNN with few layers. . . . . . . . . . . . . . . . Commented diagram of AlexNet’s architecture, based on an illustration in [263]. Notice that the network’s flow has two stacked layers dividing processing of the input image in two halves, due to the original authors’ use of two GPU’s during development. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Examples from a single scenario of the DHS dataset where certain body areas are not visible in some viewpoints. (a) Shows viewpoint number 9, where body area 5 (the chest) is not visible, and (b) is the complimentary case; viewpoint 1 where body area 17 (the upper back) is not seen. . . . . . . . . . . . . . . .. 158. 159 160 161 163 163. 164. 168.

(30) xvi. 4.9 4.10 4.11 4.12 4.13 4.14. 4.15 4.16 4.17 4.18. 4.19 4.20 4.21 4.22 4.23. 4.24. 4.25. 4.26. 4.27. 4.28. 4.29 4.30 4.31 4.32. LIST OF FIGURES. Probability of threat existence in each body zone for the labeled images in the DHS dataset. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168 Sample comparison between an original (a) and processed (b) image from one of the DHS dataset’s scenarios. . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Average training duration in minutes for each network in the model. . . . . . 170 Evolution of training accuracy for three of the model’s CNNs. . . . . . . . . . 171 Diagram of a generic confusion matrix for a 2-class classifier. . . . . . . . . . 172 Confusion matrices for all 17 CNNs in the developed model, in sequential order, left to right and top to bottom. The vertical axis in each chart represents the predicted class, while the horizontal axis represents the actual class. Matrix layout follows the diagram in Fig. 4.13. . . . . . . . . . . . . . . . . . 173 Evolution of training loss for three of the model’s CNNs. . . . . . . . . . . . 174 Training loss for each CNN in the developed model. . . . . . . . . . . . . . . 174 Weight representation for the first convolutional layer of all 17 CNNs in the developed model, in sequential order, left to right and top to bottom. . . . . 176 Sample processed image from the DHS dataset showing a person with an oval threat in the upper chest area (body region 5). Threat boxed in blue, only for this display . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 Activation maps for network 5’s first convolutional layer when input with the image in Fig. 4.18. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178 Comparison of the image from Fig. 4.18 and the maximum activation map from Fig. 4.19. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 Activation maps for network 5’s fifth convolutional layer when input with the image in Fig. 4.18. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 Comparison of the image from Fig. 4.18 and the maximum activation map from Fig. 4.21. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 (a) Comparison of the image from Fig. 4.18 and the maximum activation map from the network’s ReLu layer after the fifth convolutional layer, (b) along with an overlay of the two after coloring the activation map in red. . . . . . . 181 Comparison of a processed image from the DHS dataset showing a person with no threat in the upper chest area (body region 5) and the maximum activation map from the network’s ReLu layer after the fifth convolutional layer. . . . . 181 Threats included in the IR dataset, in order from left to right: ice pick, 9 mm handgun, ceramic knife, shrapnel explosive (plasticine+bolts), water bottle, IED (capacitors). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 Sample frames from an imaging scenario in the IR dataset where a male subject hides a cool water bottle underneath a T-shirt. The frames show the four ∼90◦ viewpoints the subject is asked to maintain during one minute each. Colorbar: Temperature in degrees. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184 Sample frames from the scenario in Fig. 4.26 taken just 1 second apart. Frame (a) is labeled as a threat scenario because it displays the full bottle, while frame (b) is labeled a no threat scenario because it only shows part of the object. Colorbar: Temperature in degrees. . . . . . . . . . . . . . . . . . . . . . . . . 184 (a) Raw IR frame and (b) cropped result. In the cropped example all unnecessary shapes in the image have been erased. Colorbar: Temperature in degrees. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 Distribution of images from the IR dataset into the 7 threat classes considered.185 Average image of the IR dataset generated for image diversity calculation. . . 186 Training (a) accuracy and (b) loss for the CNN when inputing images of a single subject hiding cooled threats placed in his torso underneath a T-shirt. 187 Diagram of a generic confusion matrix for a N -class classifier focusing on class ck . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187.

(31) LIST OF FIGURES. xvii. 4.33. Confusion matrices for (a) training and (b) validation for the CNN when inputing images of a single subject hiding cooled threats placed in his torso underneath a T-shirt. The vertical axis in each chart represents the predicted class, while the horizontal axis represents the actual class. Matrix layout follows the diagram in Fig. 4.32. . . . . . . . . . . . . . . . . . . . . . . . . . 188. 4.34. Comparison between the two male subjects considered in the dataset for the same threat scenario (a concealed handgun). . . . . . . . . . . . . . . . . . . 189. 4.35. Confusion matrices during testing for the CNN when inputing images of two male subjects when the training images come from (a) a single subject or (b) both subjects. The vertical axis in each chart represents the predicted class, while the horizontal axis represents the actual class. Matrix layout follows the diagram in Fig. 4.32. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190. 4.36. Sample images of additional scenario variations considered in the full IR dataset, such as (a) uncooled threats, (b) thicker clothing, or (c) random threat placements. In all images the subject is hiding the same handgun. . . 191. 4.37. Confusion matrices during testing for the CNN when inputing images from the full IR dataset, after it has been trained with (a) a subset or (b) the full dataset. The vertical axis in each chart represents the predicted class, while the horizontal axis represents the actual class. Matrix layout follows the diagram in Fig. 4.32. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192. 5.1. Diagrams of (a) the sensor as it moves through a road with a guide installed, and (b), (c) of the plastic guide with a codification based on two levels of cavity depth. The sensor’s movement (vector V ) is used to scan the guide. The set-up’s axes, used throughout the text, are also depicted. . . . . . . . . 199. 5.2. Maximum vehicle speed for the proposed technique as a function of bit size (lbit ), chirp time (Tchirp ), and number of chirps integrated per bit (n). . . . . 201. 5.3. Block diagram and photograph of the RF architecture of the radar used during measurements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203. 5.4. Diagram (a) and photograph (b) of the focusing scheme for the proposed setup. Dielectric lenses are used to focus the transmission onto an area small enough to cover only a single bit. F1 and F2 are the lenses’ focal lengths (5 cm and 20 cm, respectively). . . . . . . . . . . . . . . . . . . . . . . . . . . . 203. 5.5. Diagram (a) and photograph (b) of the radiation scheme for the proposed set-up. The MMIC-based radar’s HDPE lens is used to collimate radiation on the OUT. The distance to the OUT is set to 32.5 cm, which is the working range taking into account the focal lengths of the lenses in Fig. 5.4, to offer comparable results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204. 5.6. (a) Comparison of uncalibrated, calibrated and ideal spectrums received from a metallic sheet, as captured by the 300 GHz radar. (b) Set-up used for the measurement of the calibration’s reference signal from a metal sphere. . . . . 205. 5.7. Measurements at (a) 300 GHz and (b) 122 GHz (Configuration 3) of a metallic sheet, a solid slab of HDPE, and a mm-wave absorber. RX bandwidth is 20 MHz, sweep period is 150 µs, and a Hamming window is applied. . . . . 205. 5.8. XY spot size measurement at (a) 300 GHz and (b) 122 GHz (Configuration 3), with a return loss criteria, and using metallic coins of various sizes as targets.206. 5.9. Range measurement sequence at (a) 300 GHz and (b) 122 GHz (Configuration 3) with a 1A C coin moving 10 cm back and front starting from 32.5 cm, in 1 cm steps, to analyze tolerance to variations of target placement in the Z axis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207.

(32) xviii. 5.10. 5.11. 5.12. 5.13 5.14. 5.15 5.16. 5.17. 5.18. 5.19 5.20. 5.21. 5.22. 5.23 5.24 5.25. LIST OF FIGURES. Measurement of losses due to the sensor’s orientation shifts in the Z axis at (a) 300 GHz and (b) 122 GHz (Configuration 3). A 0◦ shift means the target’s surface normal aims directly at the radar. For the 122 GHz radar, the measurements performed include data with and without its dielectric lens. . . 208 Spectrum of solid plastic slab in different humidity scenarios at (a) 300 GHz and (b) 122 GHz (Configuration 3). In the wet scenarios water is applied with a diffuser on the surface. In the film scenario plastic film is placed over the wet surface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208 Spectrum of a block of road asphalt at (a) 300 GHz and (b) 122 GHz (Configuration 3), compared against the received reflections from a metallic sheet, a solid HDPE slab, and a mm-wave absorber. . . . . . . . . . . . . . . 209 Photograph of the first two 15x2.5x5 cm3 HDPE guides designed, with (a) internal-gap and (b) surface information. . . . . . . . . . . . . . . . . . . . . 210 Spectrum of the bit columns in Fig. 5.13(a) at 300 GHz. Columns are numbered according to their position in Fig. 5.13(a), starting at the left side of the photograph. The numbers in parenthesis are the error, produced by Z-axis resolution and FFT precision, in the measurement of distance for each surface. 211 Spectrum of the first column (starting from the left) in Fig. 5.13(a), for Configuration 3 of the 122 GHz radar. . . . . . . . . . . . . . . . . . . . . . . . 211 Continuous scan of a solid HDPE slab at (a) 300 GHz and (b) 122 GHz (Configuration 3), with a -50 dBm threshold. Range variations due to propagation through different media are uncompensated. Colorbar: Received power in dBm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212 Continuous scan of the guide in Fig. 5.13(a) at (a) 300 GHz and (b) 122 GHz (Configuration 3), starting at the leftmost column, with a -50 dBm threshold. Range variations due to propagation through different media are uncompensated. Colorbar: Received power in dBm. . . . . . . . . . . . . . . 212 Continuous measurement sequence for the guide in Fig. 5.13(b) at (a) 300 GHz and (b) 122 GHz (Configuration 3), starting at the rightmost column and with the bits at the back surface, with a -50 dBm threshold. Range variations due to propagation through different media are uncompensated. Colorbar: Received power in dBm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 Photograph of an HDPE guide based on triangular gaps as bits and diagram of the radar orientations considered when measuring the guide. . . . . . . . . 214 Continuous measurement sequence for the guide in Fig. 5.19 at 300 GHz, with a -50 dBm threshold. In (a) the radar is placed in the first orientation, according to Fig. 5.19(b), while in (b) the radar is in the second orientation of that figure. Range variations due to propagation through different media are uncompensated. Colorbar: Received power in dBm. . . . . . . . . . . . . 214 Guide in Fig. 5.19 for the (a) first and (b) second orientations considered, for Configuration 3 of the 122 GHz radar. Range variations due to propagation through different media are uncompensated. Colorbar: Received power in dBm.215 Hybrid item-labeling system in container storage, where different tagging technologies are used at different storage levels to optimize each technique’s advantages. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216 (a) Photograph and (b) diagram of the measurement set-up for vital sign detection and tracking with the 122 GHz radar. . . . . . . . . . . . . . . . . 220 Block diagram displaying the signal processing stages for vital sign monitoring with the 122 GHz radar. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 (a) Measured chest displacement during the subject’s low breathing rate scenario. (c) Measured displacement during the subject’s high breathing rate scenario. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221.

Referencias

Documento similar

This Turnstile junction had a great performance over the band between 10 and 19 GHz (see again Fig. 6.21), so it was worth to make a little effort to design better routing

In addition to traffic and noise exposure data, the calculation method requires the following inputs: noise costs per day per person exposed to road traffic

● Correlation plots allow us to infer the spectral index of the whole emission in the region.. ● Correlations between WMAP 23, 33 GHz and QUIJOTE 11, 13, 17 and 19 GHz are computed

Keywords: Millimeter wave images, 94 GHz, image simulation, synthetic database, biometrics, image process- ing, body geometry, distance-based features, feature extraction,

It is important to stress that the overall free energy jump in H-AdResS (∆F = F (0), recall that we impose F (1) = 0 as reference value) is a thermodynamic quantity which should

The timings were obtained in an otherwise idle cluster with 30 nodes, each with two dual-core AMD Opteron 2.2 GHz CPUs and 6 GB RAM, running Debian GNU/Linux and a stock 2.6.8

1) Previamente, se carga un archivo de datos que contiene las medidas tomadas a cada objeto, en diversas posiciones y en diversos momentos distintos. Es importante hacer esta

3 shows the results obtained for the normalized phase and attenuation constant of the desired leaky-wave mode (at the frequency of 50 GHz), by varying the width and position of